Assessing AI-Driven Information Retrieval Systems for Real-Time Marine Traffic Monitoring

Abstract

The increasing global maritime traffic brings in a new requirement for intelligent monitoring systems that provide prompt and precise information. Conventional systems dealing with ship traffic are struggling to cope with the massive heterogeneous data streams. These systems are suffering from increasing delays, which hinder situational awareness and increase operational risks. Striking this balance motivates the author to concentrate on the end-to-end assessment of real-time monitoring of maritime traffic using information retrieval systems based on AI. The framework designed for this research includes all data sources, including real-time Automatic Identification System (AIS) data and sensor readings with advanced real-time analysis, ensuring maximum the value of information from data acquisition, processing and interpretation. The author also incorporates machine learning and deep learning technologies for unstructured maritime data natural language processing and predictive modeling with trend and anomaly detection pattern recognition. The model undergoes an evaluation on a diverse set of retrieval accuracy, information processing delays, traffic volume, and scenario-based scalability metrics. The results documented in this work have demonstrated remarkable improvements over the current approaches that have adopted AI along with awareness, operational efficiency, and navigational safety in single- or multi-domain environments. This activity acts as a trigger towards a sophisticated maritime base and provides further directions for the research of AI-based autonomous marine systems and operations.